Sinkron : jurnal dan penelitian teknik informatika
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ECG-Based Heart Rate Variability and KNN Classification for Early Detection of Baby Blues Syndrome in Postpartum Mothers
No full textEarly detection of baby blues syndrome plays an important role in preventing postpartum emotional disturbances from developing into more serious mental health conditions. This study proposes a simple and non-invasive approach to identify early signs of baby blues in postpartum mothers by analyzing electrocardiogram (ECG) signals using the K-Nearest Neighbor (KNN) algorithm. The ECG data were gathered through wearable sensors and processed to extract heart rate variability (HRV) features such as RMSSD, SDNN, entropy, and energy. These features were then used to train and test a KNN classification model through a five-fold cross-validation process. KNN was chosen because it is easy to implement, does not assume any specific data pattern, and works well with small datasets like those commonly found in clinical settings. Its ability to group data based on similarity makes it suitable for recognizing subtle physiological changes linked to emotional stress. The model reached an accuracy of 87.5%, with strong precision and recall scores, showing its reliability in distinguishing mothers who show early symptoms of baby blues from those who do not. Among all features, RMSSD and SDNN had the highest impact, pointing to reduced parasympathetic activity in affected individuals. These findings suggest that combining HRV analysis with a straightforward machine learning approach like KNN offers a promising, low-cost solution for early emotional screening in maternal care, especially where resources are limited
Multiple Linier Regression Analysis Effects of Education Media on Student Experience and Satisfaction
No full textThe purpose of this study is to explore the effect of quiz- and game-based learning media on student learning experiences and satisfaction. This study models the relationship between media, motivation, experience, and learning satisfaction differently from previous studies with single variables. The research method uses a multiple linear regression approach with data collected through a Likert scale questionnaire. The research subjects consisted of 107 tenth-grade students majoring in Network and Computer Engineering at SMK Bhinneka Karya Simo. Data processing was carried out using Python and Microsoft Excel software for data analysis. The results showed that the variables of media, motivation, and response had a significant effect on learning experience (p < 0.05). The regression equations obtained were: experience (Y2) = 0.5918 + 0.2594X1 + 0.2840Y1, while satisfaction (Y3) = 0.5918 + 0.2594X1 + 0.2840Y1 + 0.3239Y2. In conclusion, learning experience is mainly influenced by media and motivation, while learning satisfaction is influenced by media, motivation, and the experience itself. These findings confirm that game-based learning strategies can create more meaningful learning experiences and encourage increased student satisfaction, which can be used as a basis for improving the quality of learning in the classroom
Implementation of Case-Based E-Consultation to Handle Student’s Stress Levels
No full textDespite the declaration of the COVID-19 pandemic's end, the effects of some cases persist in the new normal era of 2023. Several cases indicate a decline in the learning motivation of students and university students, which significantly affects aspects of understanding, creativity, productivity, and learning outcomes. University students transition from learners who, during their high school years, spent more time studying online without directly interacting with peers or teachers. One of the causes of university student dropouts is internal issues due to students' inability to adapt to the university environment. The purpose of this research is to compile cases frequently experienced by university students that cause stress and lead to the decision to discontinue their studies. This is done to find solutions and prevent similar incidents from recurring. The implementation of e-counselling helps provide solutions in the form of action recommendations on how to address student issues. We conducted the research in several stages, including data collection, literature review, modelling, model evaluation, and prototype building and testing. We obtained the solution to the collected cases from the counsellor through a focus group discussion (FGD). This research employs case-based reasoning, utilizing four reasoning processes: retrieve, reuse, revise, and retain. We chose the modified weighted average similarity function to measure the case's similarity value with the cases in the case base. Through the case-based e-counselling system, the calculation results reveal the similarity between the new case and the old cases, recommending actions that counsellors have validated as valid solutions
Embedded Smart Farming System for Soil and Hydroponic Planting Media Based on The Internet of Things
No full textSmart agricultural technology by applying the internet of things (IoT) purposes to make farmers' work more efficient due to the automation system and assist farmers in monitoring the condition of their agricultural land. The focus of discussion in this research is the application of smart agriculture system technology that uses the concept of embedded systems for soil and hydroponic planting media. This system applies an automation system for water irrigation and fertilizer irrigation using four tanks, namely a water source, a water irrigation tank, a fertilizer tank, and a water circulation system in hydroponics. The system is also equipped with weather monitoring based on temperature, rainfall, and light intensity. Other parameters contained in this system are soil pH, water pH, TDS, fertilizer availability, and irrigation pump status. The monitoring system based on the Android application displays all parameters and the status of the devices used
Analyzing PEGASUS Model Performance with ROUGE on Indonesian News Summarization
No full textText summarization technology has been rapidly advancing, playing a vital role in improving information accessibility and reducing reading time within Natural Language Processing (NLP) research. There are two primary approaches to text summarization: extractive and abstractive. Extractive methods focus on selecting key sentences or phrases directly from the source text, while abstractive summarization generates new sentences that capture the essence of the content. Abstractive summarization, although more flexible, poses greater challenges in maintaining coherence and contextual relevance due to its complexity. This study aims to enhance automated abstractive summarization for Indonesian-language online news articles by employing the PEGASUS (Pre-training with Extracted Gap-sentences Sequences for Abstractive Summarization) model, which leverages an encoder-decoder architecture optimized for summarization tasks. The dataset utilized consists of 193,883 articles from Liputan6, a prominent Indonesian news platform. The model was fine-tuned and evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric, focusing on F-1 scores for ROUGE-1, ROUGE-2, and ROUGE-L. The results demonstrated the model's ability to generate coherent and informative summaries, achieving ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.439, 0.183, and 0.406, respectively. These findings underscore the potential of the PEGASUS model in addressing the challenges of abstractive summarization for low-resource languages like Indonesian language, offering a significant contribution to summarization quality for online news content
Capability-Based API Gateway Technology Selection Analysis for Banking Cybersecurity Solution Using AHP Method
No full textThe growing reliance on APIs in the banking sector, driven by digital transformation, necessitates robust API Gateways that balance performance with strong security measures to address risks like API abuse, man-in-the-middle attacks, and data scraping, while ensuring compliance with regulations such as PCI-DSS, GDPR, and OJK standards. This study bridges the gap in technical guidance by developing a comprehensive evaluation framework using the Analytic Hierarchy Process (AHP) to determine the most suitable API Gateway for banking. The findings identify Apigee as the optimal choice, scoring 1.4277 for its superior authentication, traffic encryption, threat detection, deployment flexibility, cloud integration, and API management. IBM API Connect, scoring 0.6186, is a strong alternative with excellent security and management features but limited scalability and deployment flexibility. Kong and Axway API Gateway follow with scores of 0.4215 and 0.4627, excelling in deployment and integration but lacking critical security features for banking. This research emphasizes the strategic importance of selecting the right API Gateway to bolster cybersecurity and API management in banking, recommending Apigee as the primary solution and IBM API Connect for complex IT infrastructures. It also contributes to the literature by providing a structured, quantitative approach to API Gateway selection and suggests future research exploring AI integration, advanced analytics, and cost-benefit analyses for informed decision-making in the financial sector
Comparative Analysis of Homogeneous and Heterogeneous Ensembles for Diabetes Classification Optimization
No full textDiabetes mellitus is a chronic disease with an increasing prevalence worldwide, including in Indonesia, reaching 11.7% by 2023. Early prediction of this disease is essential for more effective management. This study aims to develop a diabetes mellitus prediction model using an ensemble learning approach, including homogeneous (boosting and bagging) and heterogeneous (stacking and blending) techniques. In this study, the boosting algorithm using AdaBoost with Random Forest as the base estimator showed the highest accuracy of 98%, with balanced precision and recall. The bagging technique, which also uses Random Forest as the base estimator, achieved 97% accuracy, although slightly lower than boosting. The stacking technique, which combines XGBoost, Gradient Boosting, and Random Forest as base learners, with Random Forest as the meta-model, yields similar accuracy of 98%, but with lower prediction error, demonstrating its ability to cope with more complex data. Blending, which uses a similar approach but with training on the entire dataset, gave 98% accuracy with shorter processing time and more efficient memory usage than stacking
Optimizing Software Development Through Flow Metrics Analysis in the Scaled Agile Framework (SAFe)
No full textTo meet the ever-changing market demands, more efficient strategies are required due to the complexity of today's software development. Because it can combine Agile concepts with organizational structures to manage large-scale projects involving multiple teams and departments, the Scaled Agile Framework (SAFe) has been widely adopted. This research investigates how Flow Metrics from the SAFe framework can be used as a tool to improve productivity, efficiency and alignment of the software development process. This research examines how measurements such as flow velocity, flow efficiency, flow time, and flow load can be used to pinpoint bottlenecks, streamline processes, and improve the value delivered to clients. This research uses a qualitative methodology to examine the use of Flow Metrics in two interdependent Program Increments (PIs) by combining interviews with Agile practitioners and a literature survey. The analysis highlights how the Continuous Delivery (CD) Pipeline, backlog synchronization, and program increment planning—three critical components of SAFe—interact with each other. By highlighting the importance of metrics-based performance evaluation, collaborative planning, and continuous improvement, the findings of this research are intended to offer a useful foundation for businesses looking to implement SAFe for large-scale software development. This research advances a more comprehensive understanding of how SAFe and Flow Metrics can facilitate increasingly complex software development while guaranteeing adaptability to changing business needs
Mobile Learning Application on Two-Dimensional Figure Material for Children with Intellectual Disabilities
No full textMobile learning apps are widely acknowledged for their effectiveness in enhancing learning results. This study aims to develop and validate an mobile learning app for computer two dimensional figure. Using the userfriendly App figma platform known for visual programming, it integrates interactive modules and multimedia for diverse learning styles. The study adopted a Research and Development approach following the ADDIE model (analysis, design, development, implementation, and evaluation). The research was conducted at SKh YKDW 02 Tangerang and involved 6 students. The outcomes pertaining to validation experts percentage scores are as follows: The aspect of media and design received a score percentage of 91,25%, affirming its very valid. Students responses the average percentage for the four assessment aspects clarity of material, motivation, interest, and easy of use navigation reached 91,10%, placing it in the very good category. The development of this mobile learning application for two dimensional figure material for children with intellectual disabilities material demonstrates significant potential as an innovative educational tool
Sentiment Analysis of the Relocation of the National Capital on Social Media X
No full textThe relocation of the national capital is a national strategic development project that seeks input from the public. This research analyzes public sentiment towards the relocation of the capital city using the Lexicon SVM method with data from X social media. The analysis was conducted in two languages, namely Indonesian and English, to find out how public opinion on the relocation of Indonesia's capital city at the global level. The sentiment classification results show that in Indonesian, public sentiment tends to be balanced with a model accuracy of 86.79%, where 51.3% is positive sentiment and 48.7% is negative. Meanwhile, in English, positive sentiment is more dominant with a model accuracy of 89.64%, where 83.3% is positive sentiment and 16.7% is negative sentiment. Evaluation using confusion matrix shows that this model provides good results, with high precision, recall, and F1-score values. Visualization using WordCloud and frequency analysis of unigrams, bigrams, and trigrams showed that positive sentiments mostly discussed the development aspects and government policies, while negative sentiments highlighted the social and economic impacts of the relocation. In addition, further analysis shows that public sentiment fluctuates based on important government announcements and major events related to the project. These findings demonstrate the importance of monitoring public opinion over time to understand shifts in perception. This research provides insights to the government and policymakers in understanding public opinion regarding the relocation of the nation's capital. By understanding sentiment patterns, more appropriate policies can be designed to increase public acceptance of the project and address public concerns effectively